{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "title: Notebook Ola with Slider 2\n",
    "description: Very simple notebook to show how Mercury works. Perfect for start.\n",
    "author: Piotr\n",
    "date: 2021-12-10\n",
    "show-code: True\n",
    "show-prompt: False\n",
    "params:\n",
    "  year:\n",
    "    label: \"Please select year\"\n",
    "    value: 2021\n",
    "    input: slider\n",
    "    min: 2010\n",
    "    max: 2022\n",
    "    step: 1\n",
    "  year2:\n",
    "    label: \"Please select year2\"\n",
    "    value: [2019, 2021]\n",
    "    input: range\n",
    "    min: 2010\n",
    "    max: 2022\n",
    "    step: 1\n",
    "  region:\n",
    "    label: \"Region\"\n",
    "    value: \"Europe\"\n",
    "    input: select\n",
    "    choices: [\"North America\", \"Europe\", \"Asia\", \"Africa\", \"South America\", \"Autralia\"]\n",
    "  flag:\n",
    "    label: \"Flag\"\n",
    "    value: True\n",
    "    input: checkbox\n",
    "  threshold:\n",
    "    label: \"Threshold\"\n",
    "    value: 0.5\n",
    "    input: numeric\n",
    "    min: 0\n",
    "    max: 1\n",
    "    step: 0.01\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 2021\n",
    "region = \"Europe\"\n",
    "flag = False\n",
    "threshold = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test\n",
    "\n",
    "\n",
    "\n",
    "This is example notebook\n",
    "\n",
    "## No parameters\n",
    "\n",
    "Right now there are now parameters, just run"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(np.random.rand(100, 5), columns=[\"a\", \"b\", \"c\", \"d\", \"e\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.255040</td>\n",
       "      <td>0.614577</td>\n",
       "      <td>0.163844</td>\n",
       "      <td>0.871512</td>\n",
       "      <td>0.982922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.753082</td>\n",
       "      <td>0.737448</td>\n",
       "      <td>0.340134</td>\n",
       "      <td>0.060951</td>\n",
       "      <td>0.278219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.251174</td>\n",
       "      <td>0.032595</td>\n",
       "      <td>0.491128</td>\n",
       "      <td>0.010542</td>\n",
       "      <td>0.740459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.113220</td>\n",
       "      <td>0.213564</td>\n",
       "      <td>0.170346</td>\n",
       "      <td>0.750587</td>\n",
       "      <td>0.900230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.303466</td>\n",
       "      <td>0.733240</td>\n",
       "      <td>0.708607</td>\n",
       "      <td>0.957383</td>\n",
       "      <td>0.058355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.717280</td>\n",
       "      <td>0.138960</td>\n",
       "      <td>0.900601</td>\n",
       "      <td>0.053225</td>\n",
       "      <td>0.286964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.414760</td>\n",
       "      <td>0.738950</td>\n",
       "      <td>0.118290</td>\n",
       "      <td>0.740507</td>\n",
       "      <td>0.157526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.505411</td>\n",
       "      <td>0.163696</td>\n",
       "      <td>0.942054</td>\n",
       "      <td>0.558562</td>\n",
       "      <td>0.685508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.990946</td>\n",
       "      <td>0.990682</td>\n",
       "      <td>0.367041</td>\n",
       "      <td>0.112801</td>\n",
       "      <td>0.537230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.359366</td>\n",
       "      <td>0.943180</td>\n",
       "      <td>0.696210</td>\n",
       "      <td>0.957429</td>\n",
       "      <td>0.306914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           a         b         c         d         e\n",
       "0   0.255040  0.614577  0.163844  0.871512  0.982922\n",
       "1   0.753082  0.737448  0.340134  0.060951  0.278219\n",
       "2   0.251174  0.032595  0.491128  0.010542  0.740459\n",
       "3   0.113220  0.213564  0.170346  0.750587  0.900230\n",
       "4   0.303466  0.733240  0.708607  0.957383  0.058355\n",
       "..       ...       ...       ...       ...       ...\n",
       "95  0.717280  0.138960  0.900601  0.053225  0.286964\n",
       "96  0.414760  0.738950  0.118290  0.740507  0.157526\n",
       "97  0.505411  0.163696  0.942054  0.558562  0.685508\n",
       "98  0.990946  0.990682  0.367041  0.112801  0.537230\n",
       "99  0.359366  0.943180  0.696210  0.957429  0.306914\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f464173a7c0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3])"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "991422a4b48aafa485e847ad31d5971636a1d98c72d6ea0509be42fc1cb4c24b"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": ""
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}